diff --git a/diagnostics/flow_dep_diag/flow_dep_diag.py b/diagnostics/flow_dep_diag/flow_dep_diag.py
index 15b67990c7..fbd61ebc06 100644
--- a/diagnostics/flow_dep_diag/flow_dep_diag.py
+++ b/diagnostics/flow_dep_diag/flow_dep_diag.py
@@ -1,206 +1,5 @@
-#Load libraries
-import xarray as xr
-import numpy as np
-import pandas as pd
-#import eccodes
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-import cartopy.crs as ccrs
-from cartopy import feature
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
-# key PyWR functions are imported here
-from PyWR import *
+#driver file
+import os
+import glob
 
-
-#The data used in this diagnostic was downloaded from the IRI:
-#https://iridl.ldeo.columbia.edu/?Set-Language=en
-#If data of a similar nature is desired, refer to the prep_data.py file for instructions on how 
-#to download anomaly data from the http server
-
-#need to be changed to where the files are located in framework
-reanalysis = xr.open_dataset('WUS/data/hgt_NNRP_rean.nc', decode_cf = True, decode_times = True).stack(time=['T'], grid=['Y', 'X'])
-rainfall = xr.open_dataset('WUS/data/rainfall_cpc.nc', decode_cf = True, decode_times = True).stack(time=['T'], grid=['Y', 'X'])
-t2m = xr.open_dataset('WUS/data/t2m_cpc.nc', decode_cf = True, decode_times = True).stack(time=['T'], grid=['Y', 'X'])
-uwnd = xr.open_dataset('WUS/data/u_NNRP_rean.nc', decode_cf = True, decode_times = True).stack(time=['T'], grid=['Y', 'X'])
-uwnd = xr.open_dataset('WUS/data/v_NNRP_rean.nc', decode_cf = True, decode_times = True).stack(time=['T'], grid=['Y', 'X'])
-
-#get rid of dummy pressure coordinate
-reanalysis=reanalysis.isel(P=0) 
-uwnd=uwnd.isel(P=0)
-vwnd=vwnd.isel(P=0)
-
-
-#viewing the data
-print(reanalysis)
-print(rainfall)
-print(t2m)
-print(vwnd)
-print(uwnd)
-
-
-#DIMENSION REDUCTION: choose a percentage of variance explained that we will require
-n_eof = get_number_eof(X=reanalysis['adif'].values, var_to_explain=0.9, plot=True)
-
-#project the data onto the leading EOFs to get the principal component time series
-#We will retain the PCA model for use later. The reanalysis_pc variable is now indexed [time, EOF]
-pca_model = PCA(n_components=n_eof).fit(reanalysis['adif'].values)
-reanalysis_pc = pca_model.transform(reanalysis['adif'].values)
-
-
-#REANALYSIS WEATHER TYPING: perform the clustering. We will manually specify the number of clusters we want to create and the number of simulations we want to run
-ncluster = 6 # use 6 WTs
-n_sim = 50 # typically 25-50 -- try 25 for quick preliminary computation only
-
-centroids, wtypes = loop_kmeans(X=reanalysis_pc, n_cluster=ncluster, n_sim=n_sim)
-class_idx, best_part = get_classifiability_index(centroids)
-print('The classifiability index is {}'.format(class_idx))
-
-
-#Now that we have identified a suitable partition, we can use it to keep only the corresponding centroid and set of weather type labels. Use centroids to define KMeans object
-best_fit = KMeans(n_clusters=ncluster, init=centroids[best_part, :, :], n_init=1, max_iter=1).fit(reanalysis_pc)
-
-# start with reanalysis
-reanalysis_composite = reanalysis.copy()
-model_clust = best_fit.fit_predict(reanalysis_pc) # get centroids
-weather_types = xr.DataArray(
-    model_clust, 
-    coords = {'time': reanalysis_composite['time']},
-    dims='time'
-)
-reanalysis_composite['WT'] = weather_types
-reanalysis_composite = reanalysis_composite.groupby('WT').mean(dim='time').unstack('grid')['adif']
-reanalysis_composite['M'] = 0
-
-wt_anomalies = [] # initialize empty list
-wt_anomalies.append(reanalysis_composite)
-
-wt_anomalies = xr.concat(wt_anomalies, dim='M') # join together
-wt_anomalies['WT'] = wt_anomalies['WT'] + 1 # start from 1
-
-
-#FIGURE: prepare a figure with rainfall and temperature composites
-#Hashed out options for adding wind arrows, and additional plot labels
-
-X, Y = np.meshgrid(reanalysis['adif'].X, reanalysis['adif'].Y)
-map_proj = ccrs.PlateCarree() #ccrs.Orthographic(-110, 10)
-data_proj = ccrs.PlateCarree()
-wt_unique = np.unique(wt_anomalies['WT'])
-figsize = (14, 8)
-
-#WT proportions
-wt=weather_types.to_dataframe(name='WT')
-wt=wt+1
-#wt.to_netcdf('data/t2m_cpc.nc', format="NETCDF4")
-wt_counts = wt.groupby('WT').size().div(wt['WT'].size)
-wt_counts
-
-xmin,xmax = reanalysis['X'].min(), reanalysis['X'].max()
-ymin,ymax = reanalysis['Y'].min(), reanalysis['Y'].max()
-
-# Set up the Figure
-plt.rcParams.update({'font.size': 12})
-fig, axes = plt.subplots(
-        nrows=3, ncols=len(wt_unique), subplot_kw={'projection': map_proj}, 
-        figsize=figsize, sharex=True, sharey=True
-    )
-
-# Loop through
-for i,w in enumerate(wt_unique):
-    def selector(ds):
-        times = wt.loc[wt['WT'] == w].index
-        ds = ds.sel(time = np.in1d(ds.unstack('time')['T'], times))
-        ds = ds.mean(dim = 'time')
-        return(ds)
-
-    # Top row: geopotential height anomalies
-    ax = axes[0, i]
-    ax.set_title('WT {}: {:.1%} of days'.format(w, wt_counts.values[i]))
-    C0 = selector(reanalysis['adif']).unstack('grid').plot.contourf(
-        transform = data_proj,
-        ax=ax,
-        cmap='PuOr',
-        extend="both",
-        levels=np.linspace(-2e2, 2e2, 21),
-        add_colorbar=False,
-        add_labels=False
-    )
-    ax.coastlines()
-    ax.add_feature(feature.BORDERS)
-    #ax.set_extent([-95, -65, -12, 12])
-
-#     # add wind arrows
-#     U = selector(uwnd).adif.values  
-#     V = selector(vwnd).adif.values
-#     magnitude = np.sqrt(U**2 + V**2)
-#     strongest = magnitude > np.percentile(magnitude, 50)
-#     Q = ax.quiver(
-#         X[strongest], Y[strongest], U[strongest], V[strongest], 
-#         transform=data_proj, 
-#         width=0.001, scale=0.8,units='xy'
-#     )
-
-    # Middle row: rainfall anomalies
-    ax = axes[1, i]
-    C1 = selector(rainfall['adif']).unstack('grid').plot.contourf(
-        transform = data_proj,
-        ax=ax,
-        cmap = 'BrBG',
-        extend="both",
-        levels=np.linspace(-2, 2, 13),
-        add_colorbar=False,
-        add_labels=False
-    )
-    ax.coastlines()
-    ax.add_feature(feature.BORDERS)
-    #ax.set_extent([-95, -75, -9, 5])
-
-    # Bottom row: tepmperature anomalies
-    ax = axes[2, i]
-    C2 = selector(t2m['asum']).unstack('grid').plot.contourf(
-        transform = data_proj,
-        ax=ax,
-        cmap = 'RdBu_r',
-        extend="both",
-        levels=np.linspace(-2, 2, 13),
-        add_colorbar=False,
-        add_labels=False
-    )
-    ax.coastlines()
-    ax.add_feature(feature.BORDERS)
-    #ax.set_extent([-95, -70, -9, 5])
-    ax.tick_params(colors='b')
-
-# # Add Colorbar
-plt.tight_layout()
-fig.subplots_adjust(right=0.94)
-cax0 = fig.add_axes([0.97, 0.65, 0.0075, 0.3])
-cax1 = fig.add_axes([0.97, 0.33, 0.0075, 0.3])
-cax2 = fig.add_axes([0.97, 0.01, 0.0075, 0.3])
-cbar0 = fig.colorbar(C0, cax = cax0)
-cbar0.formatter.set_powerlimits((4, 4))
-cbar0.update_ticks()
-cbar0.set_label(r'$zg_{500}$ anomaly [$m^2$/$s^2$]', rotation=270)
-cbar0.ax.get_yaxis().labelpad = 20
-cbar1 = fig.colorbar(C1, cax=cax1)
-cbar1.set_label('Precip. anomaly [mm/d]', rotation=270)
-cbar1.ax.get_yaxis().labelpad = 20
-cbar2 = fig.colorbar(C2, cax=cax2)
-cbar2.set_label('T2m anomaly [$^o$C]', rotation=270)
-cbar2.ax.get_yaxis().labelpad = 20
-
-# Format these axes
-
-
-#Add plot labels
-# letters = string.ascii_lowercase
-# for i, ax in enumerate(axes.flat):
-#    label = '({})'.format(letters[i])
-#    t = ax.text(0.05, 0.9, label, fontsize=11, transform=ax.transAxes)
-#    t.set_bbox(dict(facecolor='white', edgecolor='gray'))
-
-# Add a quiver key
-#k = plt.quiverkey(Q, 0.9, 0.7, 1, '1 m/s', labelpos='E', coordinates='figure')
-
-fig.savefig('figs/wt_composite.pdf', bbox_inches='tight') #this needs to be changed to appropriate folder in framework
-plt.show()
\ No newline at end of file
+os.environ["pr_file"] = os.environ
\ No newline at end of file